Unsupervîsed training of brain-computer interface systems using exnectation maximization

William Speier, Jennifer Knall, Nader Pouratian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The P300 speller is a brain-computer interface (BCI) system designed to communicate language by presenting language stimuli and detecting event related potentials in a subject's electroencephalogram (EEG) signal. The target patient population is prone to fatigue, so reducing or removing this training step could increase the amount of time available to the subject for actual BCI use. We present an expectation maximization approach that trains the classifier in an unsupervised manner. A general classifier is created from a set of multiple subjects and it is then refined using the subject's unlabeled data and knowledge from the language domain. The method was tested offline on a data set of 15 healthy subjects and achieved similar performance to fully supervised methods for all subjects. This suggests that this method could be used in the place of the training step for BCI systems.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages707-710
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Country/TerritoryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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